






'''simple docstring'''







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


__lowerCAmelCase        =							logging.get_logger(__name__)




@dataclass
class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : str      			=     [
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]







  def __init__(			self   ,	**UpperCamelCase__	):






         """simple docstring"""


         for deprecated_arg in self.deprecated_args:
                if deprecated_arg in kwargs:
                       a_						     =			deprecated_arg[3:]
                       a_						     =			not kwargs.pop(UpperCamelCase__	)
                       logger.warning(
                           f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
                           f' {positive_arg}={kwargs[positive_arg]}'	)
         a_						     =			kwargs.pop('tpu_name'   ,	self.tpu_name	)
         a_						     =			kwargs.pop('device_idx'   ,	self.device_idx	)
         a_						     =			kwargs.pop('eager_mode'   ,	self.eager_mode	)
         a_						     =			kwargs.pop('use_xla'   ,	self.use_xla	)
         super().__init__(**UpperCamelCase__	)

  _a  : str      			=     field(
      default=__A							,       metadata={'''help''': '''Name of TPU'''}							,       )
  _a  : int      			=     field(
      default=0							,       metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}							,       )
  _a  : bool      			=     field(default=__A							,       metadata={'''help''': '''Benchmark models in eager model.'''}       )
  _a  : bool      			=     field(
      default=__A							,       metadata={
          '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
      }							,       )







  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         requires_backends(self   ,	['tf']	)
         a_						     =			None
         if self.tpu:
                try:
                       if self.tpu_name:
                              a_						     =			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name	)
                       else:
                              a_						     =			tf.distribute.cluster_resolver.TPUClusterResolver()
                except ValueError:
                       a_						     =			None
         return tpu







  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         requires_backends(self   ,	['tf']	)
         if self.is_tpu:
                tf.config.experimental_connect_to_cluster(self._setup_tpu	)
                tf.tpu.experimental.initialize_tpu_system(self._setup_tpu	)

                a_						     =			tf.distribute.TPUStrategy(self._setup_tpu	)
         else:
                # currently no multi gpu is allowed
                if self.is_gpu:
                       # TODO: Currently only single GPU is supported
                       tf.config.set_visible_devices(self.gpu_list[self.device_idx]   ,	'GPU'	)
                       a_						     =			tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'	)
                else:
                       tf.config.set_visible_devices([]   ,	'GPU'	)  # disable GPU
                       a_						     =			tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'	)

         return strategy







  @property
  def    _a       (			self	):






         """simple docstring"""


         requires_backends(self   ,	['tf']	)
         return self._setup_tpu is not None







  @property
  def    _a       (			self	):






         """simple docstring"""


         requires_backends(self   ,	['tf']	)
         return self._setup_strategy







  @property
  def    _a       (			self	):






         """simple docstring"""


         requires_backends(self   ,	['tf']	)
         return tf.config.list_physical_devices('GPU'	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         requires_backends(self   ,	['tf']	)
         if self.cuda:
                return len(self.gpu_list	)
         return 0







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self.n_gpu > 0












'''simple docstring'''







from math import isqrt, loga


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[True] * max_number
       for i in range(2   ,       isqrt(max_number - 1   ) + 1   ):
              if is_prime[i]:
                     for j in range(i**2   ,       lowercase_   ,       lowercase_   ):
                            a_						     =			False

       return [i for i in range(2   ,       lowercase_   ) if is_prime[i]]


def       __UpperCamelCase					(      lowercase_       :  int = 800_800   ,       lowercase_       :  int = 800_800   ):
       """simple docstring"""




       a_						     =			degree * loga(lowercase_   )
       a_						     =			int(lowercase_   )
       a_						     =			calculate_prime_numbers(lowercase_   )

       a_						     =			0
       a_						     =			0
       a_						     =			len(lowercase_   ) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]   )
                  + prime_numbers[left] * loga(prime_numbers[right]   )
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
  print(f"""{solution() = }""")




